International Journal of Mechanical Engineering and Robotics Research
IJMERR is a scholarly peer-reviewed international scientific journal published bimonthly, focusing on theories, systems, methods, algorithms and applications in mechanical engineering and robotics.
A New Adaptive Neural Fuzzy Sliding Control Method for Dynamic Nonlinear Plants
This paper proposes a New Adaptive Neural Fuzzy Sliding Mode Controller (NANFSMC) for the regulation of a Coupled Tank System (CTS), targeting the adaptive control of unknown dynamic nonlinear systems subject to external disturbances, sensor noise, and other uncertainties in experimental environments. The CTS exhibits strong nonlinearities and uncertainties arising from sensor noise, parameter variations, variations in output valve characteristics, and significant time delays. The proposed control architecture integrates two synergistic components. The first is an adaptive control component that employs a Radial Basis Function Neural Network (RBFNN) to approximate the adaptive control law, with an adaptive updating mechanism in compensating for RBFNN approximation errors. The second is a sliding mode control component whose parameters are updated in real time via a fuzzy inference mechanism to enhance robustness. Both control laws are derived within the framework of Lyapunov stability theory, ensuring closed-loop stability under all operating conditions. The proposed approach is experimentally validated and benchmark in comparison with an adaptive neural sliding mode ANSMC and an optimal PID controller. Experimental results demonstrate that the proposed NANFSMC achieves superior tracking performance, robust stability, and adaptability to diverse uncertainties, including sensor noise, external disturbances, and time-varying reference signals.
novel adaptive neural fuzzy sliding mode (NANFSMC) algorithm, coupled tank system (CTS)
Copyright (c) 2026 International Journal of Mechanical Engineering and Robotics Research
Particle Swarm Optimization-Based Hazardous Configuration Identification and Dynamic Analysis for Industrial Robots
This paper takes the KUKA KR3 R540 six-degree-of-freedom (6-DOF) industrial robot as the research object. Based on the kinematic model established using the standard D-H method, the Newton-Euler dynamic theory is introduced to construct the multi-body dynamic model of the robot. To identify the dangerous working postures that cause the maximum joint torque, a search strategy based on the Particle Swarm Optimization (PSO) algorithm is proposed, and Adams-MATLAB co-simulation is adopted for solution. With the maximization of the sum of absolute values of each joint driving torque as the optimization objective and joint angles as decision variables; MATLAB drives the PSO algorithm to generate candidate postures and calls Adams for high-precision dynamic solution, automatically traversing the high-dimensional joint space and iteratively searching for the global optimal solution. Simulation results show that this method can effectively locate the global most hazardous configuration and obtain the corresponding maximum total torque for each joint, providing crucial load boundary conditions for the robot's structural strength verification and lightweight design, while also offering references for the selection of key components for structural optimization.
Topic: Autonomous Robots in Industry 4.0
KUKA robot; Dynamic analysis; Hazardous configuration; PSO; Adams-MATLAB co-simulation
Dynamics-Driven Policy Transfer to Heavy Wheel-Legged Robots via Imitation and Optimization
Policy transfer is an efficient approach for developing specific robots. Its effectiveness depends on high-quality imitation datasets and a stable learning process. However, substantial differences in geometry and dynamics between source and target robots pose challenges. Purely kinematics-driven mapping methods and manual parameter tuning often fail to maintain kinematic-dynamic consistency. In this study, we transfer control policies from the quadruped robot Unitree Go1 to our self-developed heavy wheel-legged robot Tiangou. We propose a Consistency-Aware Retargeting (CAR) method. This extends conventional inverse kinematics by adding dynamic consistency constraints. Using motion data from Go1’s MPC controller, CAR generates a reference dataset for Tiangou. We then integrate Bayesian Optimization (BO) into the imitation learning framework. This enables autonomous tuning of policy model structures and optimization hyperparameters. Experiments show that CAR reduces foot-end position errors, mitigates joint angular velocity fluctuations, and decreases foot-end slippage. Moreover, Bayesian optimization improves sample efficiency and training stability. These contributions establish a practical foundation for policy transfer across heterogeneous robotic platforms.
Topic: Recent Advances in Mobile Robotics Navigation
Prediction of Wear Behavior in Surface Contact using CDM and ANN models
The objective of this study is to investigate and predict wear behavior in contacting surfaces through an integrated approach that combines Continuum Damage Mechanics (CDM) and Artificial Neural Networks (ANN). Pin on disk wear experiments were performed under dry conditions using three engineering materials ST37, C45E4, and Al 7075 with variations in load, sliding speed, and hardness systematically designed using a full factorial Design of Experiments (DOE). The CDM model quantified material degradation and estimated wear coefficients, which were then used as training data for a feed-forward back-propagation ANN. Both models were validated against independent experimental data. Results indicate that the ANN model achieved high prediction accuracy (average error <5%), outperforming the CDM model (average error ≤12%). Scanning Electron Microscopy (SEM) revealed adhesive wear as dominant in the steels, while Al 7075 exhibited reduced wear due to higher hardness. The interaction effects showed that load and sliding speed have significant influences on wear, whereas hardness plays a secondary role. The findings establish a robust framework for wear prediction, process optimization, and potential real-time monitoring in engineering applications, demonstrating the effective integration of physics based and data-driven modeling in predictive tribology.
Topic: Tribology: Friction, Wear, and Lubrication
Tribology; Artificial Intelligence (AI); Surface Wear; Continuum Damage Mechanics (CDM); Steady-State; Artificial Neural Network (ANN).
A Hybrid Bayesian ICA-LSTM Framework for Unsupervised-Like Anomaly Detection in Rolling Element Bearings
This study proposes a hybrid anomaly detection framework based on Independent Component Analysis (ICA) and Bayesian Long Short-Term Memory (LSTM) networks for early fault diagnosis in rolling element bearings. The model is trained solely on normal condition data from the CWRU Bearing Dataset, enabling unsupervised-like deployment suitable for real-world industrial environments. Vibration signals are preprocessed using ICA to extract statistically independent features, followed by a Bayesian LSTM that captures temporal dynamics and provides uncertainty-aware predictions. A dynamic thresholding mechanism based on Interquartile Range (IQR) is implemented to autonomously distinguish between normal and anomalous behavior without manual calibration. Experimental results demonstrate exceptional performance with 99.1% accuracy, perfect recall (100%) on fault conditions, and zero false negatives, ensuring no fault is missed. The composite anomaly score effectively tracks degradation progression, making the system highly reliable for predictive maintenance applications. This approach combines statistical signal processing with probabilistic deep learning to deliver a robust, interpretable, and adaptive solution for bearing health monitoring.
Topic: Recent Innovations in Mechanical Vibrations
Bearing fault diagnosis, anomaly detection,
The Effect of Preheating on The Quality of Spot Welding Between Dissimilar Mild Steel and Stainless Steel 304
Welding dissimilar materials often presents specific challenges due to differences in thermal properties, expansion, and chemical reactions between the joined metals. This study assesses the effects of preheating mild steel and AISI 304 stainless steel plates on tensile strength, elongation, and material hardness. The variations in preheat temperature used are 100°C, 120°C, 150°C, and 180°C. Welding mild steel and stainless steel AISI 304 while preheating shows that 180°C is the best preheating temperature for getting an average tensile strength of 12.41 kgf/mm² and an average strain of -0.655. The hardness test results show that a preheat of 120°C provides optimal hardness in the weld area (106.1 HR), HAZ (96.7 HR), and base metal (103.9 HR). More than that, spot welding between mild steel that has not been treated in any way and stainless steel AISI 304 that has been heated up shows that a temperature of 180°C for the heating up process gives the best improvement in plasticity, with a tensile strength of 15.94 kgf/mm² and an average elongation of 1.7970. Hardness test data show that preheating at 180°C provides the highest hardness in the weld area (109.30 HR), and 120°C provides the highest hardness in the HAZ (99.03 HR). In contrast, the base metal decreases hardness with increasing preheat temperature, with the lowest value at 180°C (77.63 HR). The selection of the correct preheat temperature is crucial to ensure optimal weld joint quality.
System Classification of Hand Movements in Hand Prosthesis Prototype Based on Model Machine Learning Control
The constraint of loss of limb function will cause difficulties in carrying out normal activities. Moreover, limbs, especially hands, play an important role in most human activities. Various studies on the development of automatic control of hand prostheses have been carried out. The use of appropriate sensors and the availability of certain working algorithms in the design and construction of hand prostheses are very important to improve the functionality of the hand prosthesis control system aspect. Thus, the machine learning model control system, on the one hand, the prosthesis prototype is designed to be able to move the hand automatically based on the classification algorithm carried out by machine learning. The FSR402 sensor on the force sensor reading system can be used as input for the Master Controller. In the Master Controller, there is a deep learning neural network system that uses the ANN (Artificial Neural Network) 32 neuron architecture model with an accuracy value of 99.05% and a loss value of 0.0284. The model deployed to the Master Controller produces a prediction value above 90% for all labels. The designed hand prosthesis control system has successfully moved the hand prosthesis based on ten predetermined labels.
control system, hand prosthesis, functionality, joint movement, force sensor
Path Optimization using an Improved APF-RRT* Algorithm
Path planning remains a critical research area in mobile robotics, yet current approaches often suffer from suboptimal path quality, limited sampling efficiency, and inadequate adaptability across diverse operational scenarios. To address these issues, this paper proposes an improved algorithm combining Artificial Potential Field (APF) and Restricted Path Time (RRT*) approaches. This algorithm employs an optimization model that combines dynamic sampling with potential field guidance, constructing a two-stage dynamic sampling mechanism. During sampling, candidate nodes with Gaussian noise are generated along the resultant force direction. Finally, path cost comparison and parent node reselection are performed within the dynamic optimization radius to ensure asymptotic optimality of the path. Experimental results show that in complex maps, path length is reduced by 33.41% and 26.64%, respectively, and planning time is reduced by 21.36% and 86.32%, respectively; in narrow passages, path length is reduced by 49.6% and 49.8%, respectively. The results confirm the effectiveness of the two-stage dynamic sampling mechanism, which not only preserves the probabilistic completeness of the RRT* algorithm but also adaptively adjusts the sampling strategy, improving both planning length and time.
Artificial potential field; RRT* algorithm; path planning, dynamic sampling
Modified Connective Assembly Model for Error Propagation Analysis of High-Precision Assembly of Mechanical Components
High-precision mechanical assemblies require accurate error propagation models to precisely predict error accumulation in the final assembly and to ensure optimal performance and reliability. Conventional linear models exhibit limited accuracy in predicting geometric errors. To address these limitations, this study proposes a modified connective assembly model based on second-order nonlinear error propagation using homogeneous transformation matrices. The proposed model is implemented in Python, and its effectiveness is validated against existing linear and fully nonlinear connective assembly models to evaluate its accuracy and computational efficiency. At an angular orientation error of 1°, the linear model exhibits a fixed error of 0.40 mm in the Z-direction, failing to capture the error of 0.10 mm observed in the fully nonlinear model. The developed model reduces this discrepancy to 0.23 mm, achieving a 56.67% improvement in accuracy over the linear model. Similarly, under 1 mm geometric run-out tolerance, the linear model exhibits an error of -1.60 mm in the Z-direction, while the fully nonlinear model predicts -1.70 mm error. The modified model narrows this gap to -1.66 mm and delivers a 60% improvement. Moreover, the execution time comparisons based on Monte Carlo simulation (MCS) show that the proposed model is computationally efficient, achieving a 48.39% reduction in execution time compared to the fully nonlinear model.
Developing A Vision-Guided Tracked Robot for Fire Emergency Missions
The rapid development recently in urbanization and industrial processes in Vietnam has significantly transformed living and production spaces, leading to a notable increase of fire and explosion incidents in both the frequency and complexity. Fire incidents can lead to devastating consequences to human lives, environment and national resources. Firefighting is a life-threatening profession, exposing firefighters to extreme heat, toxic smoke, collapsing structures, and explosive hazards. These risks make rescue and fire suppression operations highly dangerous and complex. As urbanization and industrialization intensify fire incidents, there is an urgent need to deploy intelligent firefighting robots. Equipped with sensors, cameras, and autonomous navigation, these robots can operate in hazardous environments, reduce human exposure, and enhance the efficiency of emergency response. To address the life-threatening risks associated with firefighting operations, this study presents the design and control of a tracked autonomous firefighting robot capable of navigating complex terrains and detecting fire hazards through a vision-guided approach. The robotic system integrates a machine vision module, centered on the YOLOv8 deep learning model for real-time fire detection. A depth camera is employed to capture environmental data, from which a deviation angle and distance to the fire source are computed and transmitted to a PLC-based control unit via Modbus RS485 communication protocol. For autonomous navigation and obstacle avoidance, the robot utilizes a Bug 2 algorithm, informed by LiDAR sensor input. Experimental evaluations demonstrate the robot’s ability to accurately detect fire sources and efficiently maneuver around obstacles, validating its potential as a reliable solution for hazardous firefighting scenarios.
Firefighting robot, autonomous control, YOLO8, obstacle avoidance, fire detection
Dynamic Modeling and Control of Pneumatic Artificial Muscles-Driven Multi-Fingered Robots for Circular Object Manipulation
Recently, great concerns have been brought up when applying biologically-inspired robots in rehabilitation for individuals with movement disability. This type of robots must have a higher level of safety feature- provided by more flexible construction. Pneumatic Artificial Muscle (PAM) is driven by compressed air and its performance is similar to biological muscles. Then PAMs are great candidates of actuators for rehabilitation robots. This paper investigates a control algorithm for a multi-fingered robot actuated by PAMs in grasping and manipulating a circular-shaped object. By using the Lagrange method in combination with the natural force-length-velocity relationship of the contracting muscle, a dynamic model for the general robot-object system was formulated. Based on this dynamic model, control algorithms for stable grasping and dexterous manipulation of the object by the multi-fingered robot were proposed. The asymptotic convergence of the closed-loop dynamics was analyzed using the Lyapunov principle and the extended LaSalle invariance theorem. Simulation results further validated the effectiveness of the proposed control algorithms.
Development of Flexible Clamping Devices for Precision Workholding of Irregular and Thin-Walled Workpieces
This paper presents the design, simulation, and experimental validation of a flexible clamping fixture specifically developed for machining irregular and thin-walled workpieces components that are traditionally difficult to fixture due to their non-uniform contours and low structural stiffness. The proposed system features modular segmented jaws capable of conforming to a wide range of geometries without damaging the part or requiring custom fixture fabrication. Through detailed CAD design and finite element analysis, the fixture's structural integrity, clamping force distribution, and deformation behavior are rigorously evaluated. Experimental results confirm the system’s adaptability and effectiveness in securely holding asymmetric and delicate parts under real machining conditions. The developed fixture not only reduces setup time but also enhances positioning accuracy, repeatability, and overall productivity, offering a practical and versatile solution for modern precision manufacturing.
irregular workpieces, thin-walled workpieces, flexible clamping, fixture
Copyright (c) 2025 International Journal of Mechanical Engineering and Robotics Research
Optimization Strategies for Electric Propulsion in UAVs: A Review of Technologies, Control Systems, and Environmental Impacts
This paper presents a systematic literature review of recent developments in the optimization of electric propulsion systems for Unmanned Aerial Vehicles (UAVs). The study focuses on technological advancements, control system enhancements, and environmental impacts to provide a holistic perspective on current trends and future research directions. A total of 42 peer-reviewed articles, published between 2014 and 2024, were analyzed using the PRISMA method to ensure transparency and rigor in selection. The findings show a significant shift toward intelligent optimization techniques, especially Particle Swarm Optimization (PSO) and its hybrid variants, which are widely used for tuning Fuzzy PID controllers to enhance system performance and stability. Furthermore, innovations in lightweight composite materials and the integration of Internet of Things (IoT) technologies have demonstrated improved endurance and adaptability of UAV propulsion systems. Despite these advancements, most studies remain at the simulation stage, with limited real-world implementation. Environmental modeling, thermal-aware control, and lifecycle analysis are identified as key areas requiring further investigation. This review highlights the necessity of cross-disciplinary approaches to address the complexity of electric propulsion optimization. Recommendations for future work include the adoption of embedded ML models, experimental validation frameworks, and system-level integration with aerodynamic and mission planning subsystems.
Design, Programming, and Evaluation of an Economic Portable Muscle Activity Measurement Device
Current commercial EMG systems struggle with four main drawbacks: limited mobility, high prices, time-sensitive processing demands, and their function suitability for a wide range of clinical and research environments. This study will solve these issues through the development of an affordable compact EMG system that ensures real-time processing stability. The system design unites surface electrodes with a low-noise amplifier and a microcontroller having performance-enhanced processing ability. The design requirements specify a signal spectrum from 0 to 250 Hz and precise resolution because this combination leads to the correct measurement of muscular activities. The device performed tests through a series of experiments with healthy participants to measure signal accuracy, noise levels, and usability conditions in controlled and semi-controlled testing environments. The results showed a mean signal-to-noise ratio of 28.5 dB with accuracy levels equivalent to commercial systems, which proved that the device provides clean EMG signals across diverse conditions. Results of noise power analysis showed 0.020 µV² in the resting state but decreased to 0.012 µV² when muscles reached their peak activation strength since effective noise reduction measures were implemented. Despite its advantages including cost-effective operation along with portability, the system presents restrictions in usage with clinical patients who need additional validation of its effectiveness. The portable EMG device presents a versatile, low-cost apparatus for rehabilitation services, sports science testing, and healthcare monitoring needs and requires continuing development for practical implementation.
Special Issue: Intelligent Mechatronics Systems: Advances in Autonomous Control and Robotics (Closed)
Expert-Rule-Based Gain Adjustment for PID and Fuzzy Controllers under Step and Continuous Inputs
Robotic manipulators' ability to perform precise and repeated operations makes them indispensable in industrial settings. Even with advances in control strategies, achieving ideal performance over several input profiles remains a challenging problem. Four control systems are evaluated in this work: fuzzy logic control, fuzzy expert system for step input control, fuzzy expert system for smooth function control, and the fuzzy-based expert-rule gain adjustment method. Using smooth input profiles across joints R, T, and Z, the method examines the system's response to step setpoints of 10, 3.75, and 5, as well as larger step setpoints of 30, 11.25, and 15. The performance criteria assessed include overshoot, rise time, settling time, integral absolute error, and root mean square error. Under all testing conditions, the results show that the suggested approach consistently outperforms the others. Its low overshoot and root mean square error for step setpoints ensure accurate and consistent results. Larger step setpoints demonstrate excellent performance with balanced metrics, emphasizing scalability and robustness. Especially in joints T and Z, it shows notable adaptability to smooth input profiles and achieves low integral absolute error and root mean square error. These results highlight how effectively the system combines the advantages of step and smooth input control. This work shows that under different input conditions, the proposed control method guarantees accuracy, stability, and adaptability, offering a reliable and flexible solution for dynamic robotic systems. For practical applications, it marks a significant improvement in robotic manipulator performance.
Assoc. Prof. Dr. Phichitphon Chotikunnan
Characterization and Validation of R410A Condensation Performance in Split Air Conditioning Units: Experimental Investigation and ANSYS Fluent Simulation
This study introduces a novel approach to optimizing the condensation performance of R410A refrigerant in split air conditioning systems, offering actionable insights for industry and addressing critical challenges of energy efficiency and climate adaptation in regions such as the Kurdistan Region of Iraq. By integrating advanced experimental instrumentation with high-fidelity Computational Fluid Dynamics (CFD) modeling, it presents a comprehensive investigation of two-phase flow dynamics across the full length of an 8.914 m copper condenser tube. Forty-one precisely calibrated temperature sensors and pressure transducers provide real-time monitoring of thermal and flow behavior, while CFD simulations employ the Volume of Fluid (VOF) method with geometric reconstruction and level set interface tracking to accurately predict phase distributions and vapor void fraction. Results reveal three distinct condensation zones: a desuperheating region (0-1.8 m) where superheated vapor rapidly cools, a condensation zone (1.8-6 m) characterized by near-saturation temperatures and a sharp decline in vapor fraction, and a subcooling zone (6-8.9 m) dominated by liquid-phase sensible heat removal. The correlation between experimental and CFD results demonstrates robust model validation, with an overall prediction error of 20-22%. The study highlights the critical role of stratified two-phase flow in maximizing heat transfer efficiency and confirms that the condenser design achieves complete condensation and subcooling, ensuring only liquid refrigerant exits the system. These findings are directly relevant to manufacturers seeking to improve the performance and reliability of split air conditioning units under demanding climatic conditions, both locally and in similar environments worldwide.
Modeling of the Anti-Slip System Processes in the Wheel-Rail Contact of a New Generation Electric Locomotive
This article is devoted to the creation of a simulation model of anti-slip system processes in the "wheel-rail" contact. For this purpose, the necessary elements and model blocks were created using the specialized software environment Dymola: 1) the Sub-Wagon model of a single wagon using Modelica Mechanics Translational; 2) the traction control and speed regulation system model for the new generation KZ8A locomotive, which is compatible with other Modelica blocks and can be used instead of the ideal wheel without slip; 3) a wheel-rail contact model designed for the analysis of wheelset slip during traction and sliding during braking; 4) a computer model of the anti-slip system. Another feature of this model is the consideration of the geographical characteristics of the real Agadyr-Darya section when simulating the movement of rolling stock, which consists of a single section of the new generation KZ8A electric locomotive and 35 freight wagons. An algorithm was developed using Harel's state machine formalism, implemented in Modelica StateMachine, and experimental adhesion characteristics were obtained for various friction conditions of the rail. The developed model allows for the synthesis of an adaptive speed regulator that takes into account local wheel-rail adhesion characteristics as well as track gradients. The results of this work are aimed at improving the efficiency of rolling stock, reducing fuel/energy consumption, and minimizing wheel and rail wear, which together will result in significant economic benefits.
Reinforcement Learning-Based Adaptive Vibration Control for Smart Structures with Fuzzy Uncertainty Quantification
Vibration suppression in mechanical systems (e.g., aerospace, automotive, and robotics) demands adaptive control strategies to handle nonlinear dynamics and environmental uncertainties. Traditional PID and LQR controllers often fail under varying operational conditions. This paper proposes a hybrid framework integrating Deep Deterministic Policy Gradient (DDPG) reinforcement learning with fuzzy logic to optimize vibration damping in real time. The fuzzy system quantifies uncertainty in sensor data, while the RL agent dynamically adjusts control policies. Experimental validation on a cantilever beam with piezoelectric actuators demonstrates a 34% improvement in settling time over baseline methods under stochastic disturbances.
Adaptive control, vibration, RL_Fuzzy controller, DDPG, PID controller.
Fuzzy Logic Controller with Rplidar for Hybrid Three-Wheeled Omnidirectional Mobile Robot in Dynamic Environment
In this study investigate and development of a fuzzy control system Specifically designed for a hybrid omni mobile robot. This kind of robot is recognized as a holonomic vehicle, which have the ability to produced simultaneous and dependent translational and rotational motions. This paper focused on movement of a three wheeled omni mobile robot 3WOMR (omnidirectional) in a hybrid configuration. The system addresses the obstacle avoidance task which requires robots to move through stationary and moving obstacles on their way to reaching their targets in real-world scenarios. The developed fuzzy controller makes use of a LIDAR sensor to accomplish this functionality. The RPLIDAR sensor primarily detects obstacle distances in front of the robot by dividing this area into three equivalent segments arranged at 45-degree intervals. The system has been designed to utilize seventeen cognitive states, allowing the robot to operate with three distinct input sets, each containing three linguistic values, and two output sets, each consisting of four linguistic values. Each scenario is governed by a set of reactions that are delineated according to 27 fundamental rules. The controller uses three inputs namely Distance from the head (DH), Distance from the Left (DL), and Distance from the Right (DR) to determine the angular velocities of left-side robot (L) and right-side robot (R) wheels.
Trajectory Tracking for Autonomous Vehicles Using NMPC Method and Semantic Lane Inference
This paper introduces an integrated control system for autonomous ground vehicles, combining Nonlinear Model Predictive Control (NMPC) for lateral trajectory tracking with a fuzzy PID controller for longitudinal speed regulation. A vision-based lane detection algorithm generates the reference trajectory, which the NMPC follows, while the fuzzy PID controller adjusts throttle commands to maintain desired speeds. Simulation tests at 5.0 m/s and 10.0 m/s show the vehicle reaching target speeds within 29-to-50-time steps and achieving stable tracking with RMSE values of 7.60 m and 7.31 m. The fuzzy PID ensures smooth throttle transitions, minimising control chatter and enhancing ride quality. The system demonstrates robustness across varying speeds and is suitable for real-time use. Future efforts will incorporate obstacle avoidance and improve adaptability to dynamic road conditions.
Towards Affordable and Lightweight Humanoid Robots: A Task-Specific Design Approach
The world has entered into the Fifth Industrial Revolution, and the core concept of this revolution is the harmonization of humans and machines, emphasizing a human-centric approach to technological advancements. Humanoid robots are designed to interact with humans or work with humans in human-centric environments. However, full-size humanoid robots are still so expensive that will not be suitable for application-specific activities like classroom teaching purposes. The complex mechanical design and expensive hardware are responsible for this cost. Researchers are also facing challenges to reduce humanoid development costs. This paper proposed a cost-effective design for a humanoid robot that is capable of talking, moving, shaking hands, and communicating with humans. With 31 DOF, Dbot is capable of providing human-like motion. The main material of the mechanical structure is aluminium, which makes it more lightweight and stronger. Instead of using expensive hardware, Dbot utilizes a low-cost hardware system that is suitable for task-specific applications. This paper only discusses the design process for mechanical and hardware systems. The proposed design has been evaluated through experiments to understand the performance of both the mechanical and hardware design. After developing based on this design, Dbot was tested at Daffodil International School and College to assist in classroom teaching, and found that with this design, Dbot can successfully interact with students. This lightweight, cost-effective design represents a significant advancement in humanoid robot applications, with the potential to make humanoid robots more accessible and practical for a variety of task-specific uses.
Assessment of Dynamic Loads on a Railway Track from Open Wagons in a Worn Condition with an Increase in Axial Load
The purpose of the presented article is to evaluate the dynamic loads affecting the railway track during the operation of gondola cars in a worn-out technical condition under conditions of increased axial load. The analysis of the features of the interaction of a gondola car with the upper bed of the railway track, as well as the effect of operational wear of components and elements of trolleys on the nature of the transmitted loads, is carried out. An experimental study and simulation of the dynamics of a gondola car has been performed, taking into account the technical condition indicators, loaded and empty condition of gondolas. The results obtained during the study make it possible to identify critical parameters affecting traffic safety and wear of the railway track, as well as formulate recommendations on acceptable indicators of the coefficient of stability margin and the condition of operation of worn-out rolling stock in the study of horizontal and vertical loads.
Topic: Fracture Mechanics and Fatigue Analysis
A Hybrid Posture Stabilization Approach for the Docking Problem of Autonomous Mobile Robots
Autonomous docking of mobile robots presents significant control challenges due to nonholonomic constraints and physical limitations. This paper addresses these challenges with a dual-controller hybrid framework that achieves precise posture stabilization while respecting system constraints. The first component is an analytically derived time-invariant controller based on a polar coordinate transformation of the robot's kinematic model, enabling bidirectional motion that enhances maneuverability. The second component employs a model predictive control (MPC) approach implemented as a pointwise min-norm (PMN) controller that explicitly handles actuator constraints while tracking references generated by the first controller. This constrained optimization is formulated as a quadratic programming problem that maintains the stabilization properties of the reference signals while satisfying velocity and acceleration limits. Extensive validation through simulations and experiments demonstrates performance improvements over conventional methods, with particular emphasis on practical docking scenarios requiring specific pose constraints. The close alignment between theoretical predictions and experimental results confirms the framework's robustness and applicability to real-world docking operations.
Fault Diagnosis in Vehicle Air Conditioning Systems via Comparison of k-Nearest Neighbors and Random Forest Classification Models
This study compares the performance of k-Nearest Neighbors (kNN) and Random Forest (RF) models for fault diagnosis in vehicle air conditioning systems. A dataset of 745 validation samples and 568 test samples was used, covering seven fault conditions, including normal operation and varying levels of condenser fouling and refrigerant leakage. The models were evaluated using accuracy, precision, recall, and F1 score to measure their fault classification effectiveness. Both models achieved a validation accuracy of 91.68%, with a slight drop to 89.26% during testing, indicating similar generalization abilities. Precision remained consistent across both models, while recall and F1 scores varied. The kNN model showed higher recall, indicating better detection of true positive fault cases, while the RF model exhibited more balanced performance with stable precision and F1 scores across datasets. ROC curve analysis revealed that RF outperformed kNN, especially in distinguishing multiple fault classes with overlapping features. However, both models struggled with intermediate levels of condenser fouling, as highlighted by confusion matrix analysis, suggesting a need for improved feature differentiation. Overall, the RF model demonstrated greater stability and robustness, making it more suitable for diagnosing vehicle air conditioning faults where consistency is critical.
Design and Performance Assessment of a Mini Underwater Robot
This study focuses on the design and experimental evaluation of a mini underwater robot specifically for detecting leaks in tanks or reactors. This mini-robot are designed to overcome the challenges of inspecting and maintaining reactors, especially in areas that are difficult to access. A primary concern for effective navigation is motion stability; unstable movement can compromise data accuracy and leak detection precision. The robot operates as a remotely operated vehicle (ROV), controlled from above the water via a wired connection that supplies both power and telemetry. It is equipped with four 60 mm diameter propellers, each featuring three CW/CCW blades, and a front-mounted camera. Constructed from Polylactic Acid (PLA), the robot's dimensions are 390 x 85 x 85 mm, and it is powered by a LiPo battery. The selection of PLA offers a balance of lightweight construction and sufficient structural integrity for underwater maneuvers. Testing occurred in a site measuring 5700 x 2080 x 1100 mm, providing a controlled environment to assess the robot's performance. Experiments involved guiding the robot along various paths to assess its reliability. The results demonstrate effective control with minimal error rates on certain paths, highlighting the importance of stable movement and the potential of these mini underwater robots for intricate underwater operations. This demonstrates the potential for deploying such robots in complex industrial environments where precision and stability are paramount.
Robust Control of a 2D Rehabilitation Robot Using Admittance and RBF Neural Network
In the dynamic model of the 2D rehabilitation robot, factors such as modeling errors, uncertain external disturbances, and friction are present. To achieve compliant force interaction control, a hybrid control strategy integrating admittance control, adaptive RBF neural network compensation, and sliding mode control is proposed in this paper.First, the desired velocity is generated based on the interaction force at the robot handle through admittance control, allowing compliance to be maintained during the interaction process, thereby enhancing the comfort and safety of rehabilitation training.Second, the unknown nonlinear dynamics of the system are approximated in real time using an RBF neural network, and an adaptive learning rate mechanism is designed to accelerate the network's convergence and improve the compensation accuracy.Finally, sliding mode control is employed to compensate for neural network approximation errors and external disturbances, thereby enhancing the robustness and stability of the closed-loop system.Through simulation experiments, the proposed method has been demonstrated to outperform the pure admittance control strategy in terms of trajectory tracking accuracy, response speed, and disturbance rejection capability, verifying its effectiveness and superiority.
Admittance Control; RBF Neural Network; Sliding Mode Control; Adaptive Control; Rehabilitation Robot
Crack Interactions of Non-Coplanar Internal and External Surface Cracks of Hollow Thin Cylinders
The interaction of noncoplanar internal and external surface cracks in hollow thin cylinders plays a critical role in structural integrity assessments, particularly under cyclic and multi-axial loading conditions. This study investigates the behavior of noncoplanar parallel cracks under tension, bending, torsion, and mixed-mode loading, focusing on the distribution of normalized stress intensity factors (SIFs) and the interaction effects along the crack front. Results indicate that external cracks exhibit asymmetric SIF distributions with both amplification and shielding effects, while internal cracks primarily experience shielding. Crack depth (a/t ratio) and separation distance (s/L) significantly influence the interaction magnitude, with deeper cracks showing stronger amplification effects. Under torsional loading, shielding dominates, and interaction effects diminish with increased crack separation. Additionally, the inclination angle affects crack interaction behavior, with external cracks demonstrating greater amplification, making them more critical for structural failure. The findings provide valuable insights into crack growth behavior and fracture-mechanics in thin-walled cylinders, aiding in the development of improved damage tolerance design and life extension strategies for critical structural components.
crack interaction, non-coplanar surface cracks, hollow thin cylinder, stress intensity factor, ANSYS finite element analysis, fracture mechanics, crack assessment
Energy-Saving Research on Battery-Powered Load-Haul-Dump Machine Based on Dual Motor Drive and Intelligent Gear-shifting Control
With the increasing environmental requirements in the mining industry, battery-powered underground Load-Haul-Dump (LHD) machines have become mainstream mining equipment due to their zero emissions and low noise. However, the limited battery capacity poses a critical challenge in improving energy utilization efficiency and extending the working time per charge. This study proposes an energy-saving strategy based on dual motor drive and intelligent gear-shifting control to optimize energy efficiency. Firstly, a model of the LHD’s drive system and energy consumption is established to analyze energy usage characteristics under different working conditions. Then, an adaptive genetic algorithm is employed to optimize transmission system parameters. Furthermore, an intelligent energy-saving gear-shifting strategy based on a Backpropagation Neural Network (BPNN) is proposed to achieve precise gear control and reduce energy consumption. Simulation results indicate that the proposed strategy reduces energy consumption per working cycle by 12.7%, significantly enhancing the economic efficiency and operational performance of the LHD. This research provides a theoretical basis and engineering reference for energy-saving control of underground LHDs.
Cutting Force and Cutting Temperature Prediction in Drilling Using Finite Element Analysis
Drilling is a crucial metal cutting operation extensively employed in the mechanical engineering sector. Predicting the temperature and cutting forces (forces and torques) occurring during drilling on any material and affecting the drill bit will play a positive role in estimating the life of the drill bit and thus achieving better hole quality and accuracy. In this study, drilling force and temperature were studied during Aluminum drilling using finite element method as an alternative to experimental. This topic aims to determine the cutting force and temperature in the cutting zone by analyzing the input parameters of cutting speed and feed rate during the simulation of a high-speed steel drill bit on AL 7075-T6 material. This paper introduces a methodology for developing two finite element models: the finite element method (FEM) and smoothed particle hydrodynamics (SPH) for drilling simulation. The simulations are performed using the LSDYNA code based on available real experiments. The accuracy of the model makes it a satisfactory approach for drilling experiments. The two presented models can verify the drilling process and predict the heat and cutting forces occurring during drilling operation.
Solution of the Positioning Problem for a Planar Parallel Manipulator Based on a Third-Class Mechanism
This paper presents a kinematic analysis of a three-link manipulator system, focusing on the calculation and visualization of joint angles (q1, q2, q3) as functions of the end-effector coordinates (xS, yS) and the orientation angle (α). The goal is to determine the values of the generalized coordinates (q1, q2, q3) that allow the manipulator's working body (WB) to reach a given spatial point S in its workspace. The system's configuration and inverse kinematics problem are addressed through geometric and trigonometric constraints, with the joint angles determined by solving relevant equations involving the manipulator’s link lengths and the positioning of the end-effector. We introduce a redundancy elimination technique by minimizing the positioning error, ensuring the accuracy of the manipulator's trajectory. The results are visualized using 3D plots, which depict the relationship between the joint angles and the coordinates of the manipulator, aiding in the understanding of the system's behavior under different configurations. The proposed method demonstrates efficient handling of kinematic redundancy and offers insights into optimizing the manipulator's positioning accuracy. The findings contribute to improving the control and motion planning of robotic systems in precision tasks.